对医疗保健中机器学习的稳定性概念进行范围审查
Alan Balendran1, Céline Beji2, Florie Bouvier2
1Université Paris Cité, Université Sorbonne Paris Nord, INSERM, INRAE, Centre for Research in Epidemiology and StatisticS (CRESS), Paris, France. alan.balendran@u-paris.fr.
NPJ digital medicine
|January 17, 2025
概括
医疗保健中的机器学习 (ML) 模型有希望,但其稳定性尚不清楚. 这次审查确定了八个ML强度的概念,突出了数据和模型类型的各种方法,以更好地开发和部署.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 机器学习 稳固性 机器学习 稳固性
背景情况:
- 机器学习 (ML) 和人工智能 (AI) 解决方案在医疗保健中提供了高性能.
- 这些ML模型对扰动和环境变化的强度经常被忽视.
- 了解ML的稳定性对于安全可靠的医疗保健应用至关重要.
研究的目的:
- 系统地审查和确定文献中针对医疗保健中的ML模型所涉及的稳定性类型.
- 分析如何在各种数据类型和预测模型中应用不同的稳定性概念.
- 为利益相关者提供关于在医疗保健中导航ML模型稳定性的见解.
主要方法:
- 在PubMed,科学网,IEEE Xplore和其他来源进行了全面的文献搜索.
- 检索和分析了274个符合条件的记录.
- 根据数据类型和预测模型架构,确定并对强度概念进行了分类.
主要成果:
- 在医疗保健中的ML模型的文献中确定了八个强度的一般概念.
- 这些稳定性概念的应用和重点在使用的数据类型和预测模型上有很大差异.
- 观察到缺乏标准化的方法来评估医疗保健中的ML稳定性.
结论:
- 该研究强调了医疗保健中ML强度的多样性,有八个不同的概念出现.
- 利益相关者需要意识到在ML模型开发和部署中对稳健性的各种解释和应用.
- 需要进一步的研究和标准化,以确保在临床环境中可靠和可解释的ML模型.
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